Comparing Multilabel Classification Methods for Provisional Biopharmaceutics Class Prediction

The
biopharmaceutical classification system (BCS) is now well established
and utilized for the development and biowaivers of immediate oral
dosage forms. The prediction of BCS class can be carried out using
multilabel classification. Unlike single label classification, multilabel
classification methods predict more than one class label at the same
time. This paper compares two multilabel methods, binary relevance
and classifier chain, for provisional BCS class prediction. Large
data sets of permeability and solubility of drug and drug-like compounds
were obtained from the literature and were used to build models using
decision trees. The separate permeability and solubility models were
validated, and a BCS validation set of 127 compounds where both permeability
and solubility were known was used to compare the two aforementioned
multilabel classification methods for provisional BCS class prediction.
Overall, the results indicate that the classifier chain method, which
takes into account label interactions, performed better compared to
the binary relevance method. This work offers a comparison of multilabel
methods and shows the potential of the classifier chain multilabel
method for improved biological property predictions for use in drug
discovery and development.